Effects of Phosphorylation on Protein Backbone Dynamics and Conformational Preferences.

Phosphorylations are the most common and extensively studied post-translational modification (PTM) of proteins in eukaryotes. They constitute a major regulatory mechanism, modulating protein function, protein-protein interactions, as well as subcellular localization. Phosphorylation sites are preferably located in intrinsically disordered regions and have been shown to trigger structural rearrangements and order-to-disorder transitions. They can therefore have a significant effect on protein backbone dynamics or conformation, but only sparse experimental data are available. To obtain a more general description of how and when phosphorylations have a significant effect on protein behavior, molecular dynamics (MD) currently provides the only suitable framework to study these effects at a large scale in atomistic detail. This study develops a systematic MD simulation framework to explore the influence of phosphorylations on the local backbone dynamics and conformational propensities of proteins. Through a series of glycine-backbone peptides, we studied the effects of amino acid residues including the three most common phosphorylations (Ser, Thr, and Tyr), on local backbone dynamics and conformational propensities. We further extended our study to investigate the interactions of all such residues between position i to positions i + 1, i + 2, i + 3, and i + 4 in such peptides. The final data set comprises structural ensembles for 3393 sequences with more than 1 µs of sampling for each ensemble. To validate the relevance of the results, the structural and conformational properties extracted from the MD simulations are compared to NMR data from the Biological Magnetic Resonance Data Bank. The systematic nature of this study enables the projection of the gained knowledge onto any phosphorylation site in the proteome and provides a general framework for the study of further PTMs. The full data set is publicly available, as a training and reference set.

Arsenate reductase of Rufibacter tibetensis is a metallophosphoesterase evolved to catalyze redox reactions.

An arsenate reductase (Car1) from the Bacteroidetes species Rufibacter tibetensis 1351T was isolated from the Tibetan Plateau. The strain exhibits resistance to arsenite [As(III)] and arsenate [As(V)] and reduces As(V) to As(III). Here we shed light on the mechanism of enzymatic reduction by Car1. AlphaFold2 structure prediction, active site energy minimization, and steady-state kinetics of wild-type and mutant enzymes give insight into the catalytic mechanism. Car1 is structurally related to calcineurin-like metallophosphoesterases (MPPs). It functions as a binuclear metal hydrolase with limited phosphatase activity, particularly relying on the divalent metal Ni2+. As an As(V) reductase, it displays metal promiscuity and is coupled to the thioredoxin redox cycle, requiring the participation of two cysteine residues, Cys74 and Cys76. These findings suggest that Car1 evolved from a common ancestor of extant phosphatases by incorporating a redox function into an existing MPP catalytic site. Its proposed mechanism of arsenate reduction involves Cys74 initiating a nucleophilic attack on arsenate, leading to the formation of a covalent intermediate. Next, a nucleophilic attack of Cys76 leads to the release of As(III) and the formation of a surface-exposed Cys74-Cys76 disulfide, ready for reduction by thioredoxin.

Co-targeting HSP90 alpha and CDK7 overcomes resistance against HSP90 inhibitors in BCR-ABL1+ leukemia cells.

HSP90 has emerged as an appealing anti-cancer target. However, HSP90 inhibitors (HSP90i) are characterized by limited clinical utility, primarily due to the resistance acquisition via heat shock response (HSR) induction. Understanding the roles of abundantly expressed cytosolic HSP90 isoforms (α and ß) in sustaining malignant cells' growth and the mechanisms of resistance to HSP90i is crucial for exploiting their clinical potential. Utilizing multi-omics approaches, we identified that ablation of the HSP90ß isoform induces the overexpression of HSP90α and extracellular-secreted HSP90α (eHSP90α). Notably, we found that the absence of HSP90α causes downregulation of PTPRC (or CD45) expression and restricts in vivo growth of BCR-ABL1+ leukemia cells. Subsequently, chronic long-term exposure to the clinically advanced HSP90i PU-H71 (Zelavespib) led to copy number gain and mutation (p.S164F) of the HSP90AA1 gene, and HSP90α overexpression. In contrast, acquired resistance toward other tested HSP90i (Tanespimycin and Coumermycin A1) was attained by MDR1 efflux pump overexpression. Remarkably, combined CDK7 and HSP90 inhibition display synergistic activity against therapy-resistant BCR-ABL1+ patient leukemia cells via blocking pro-survival HSR and HSP90α overexpression, providing a novel strategy to avoid the emergence of resistance against treatment with HSP90i alone.

New simulation insights on the structural transition mechanism of bovine rhodopsin activation.

Inactive rhodopsin can absorb photons, which induces different structural transitions that finally activate rhodopsin. We have examined the change in spatial configurations and physicochemical factors that result during the transition mechanism from the inactive to the active rhodopsin state via intermediates. During the activation process, many existing atomic contacts are disrupted, and new ones are formed. This is related to the movement of Helix 5, which tilts away from Helix 3 in the intermediate state in lumirhodopsin and moves closer to Helix 3 again in the active state. Similar patterns of changing atomic contacts are observed between Helices 3 and 5 of the adenosine and neurotensin receptors. In addition, residues 220-238 of rhodopsin, which are disordered in the inactive state, fold in the active state before binding to the Gα, where it catalyzes GDP/GTP exchange on the Gα subunit. Finally, molecular dynamics simulations in the membrane environment revealed that the arrestin binding region adopts a more flexible extended conformation upon phosphorylation, likely promoting arrestin binding and inactivation. In summary, our results provide additional structural understanding of specific rhodopsin activation which might be relevant to other Class A G protein-coupled receptor proteins.

Biophysical and pharmacokinetic characterization of a small-molecule inhibitor of RUNX1/ETO tetramerization with anti-leukemic effects.

Acute myeloid leukemia (AML) is a malignant disease of immature myeloid cells and the most prevalent acute leukemia among adults. The oncogenic homo-tetrameric fusion protein RUNX1/ETO results from the chromosomal translocation t(8;21) and is found in AML patients. The nervy homology region 2 (NHR2) domain of ETO mediates tetramerization; this oligomerization is essential for oncogenic activity. Previously, we identified the first-in-class small-molecule inhibitor of NHR2 tetramer formation, 7.44, which was shown to specifically interfere with NHR2, restore gene expression down-regulated by RUNX1/ETO, inhibit the proliferation of RUNX1/ETO-depending SKNO-1 cells, and reduce the RUNX1/ETO-related tumor growth in a mouse model. However, no biophysical and structural characterization of 7.44 binding to the NHR2 domain has been reported. Likewise, the compound has not been characterized as to physicochemical, pharmacokinetic, and toxicological properties. Here, we characterize the interaction between the NHR2 domain of RUNX1/ETO and 7.44 by biophysical assays and show that 7.44 interferes with NHR2 tetramer stability and leads to an increase in the dimer population of NHR2. The affinity of 7.44 with respect to binding to NHR2 is Klig = 3.75 ± 1.22 µM. By NMR spectroscopy combined with molecular dynamics simulations, we show that 7.44 binds with both heteroaromatic moieties to NHR2 and interacts with or leads to conformational changes in the N-termini of the NHR2 tetramer. Finally, we demonstrate that 7.44 has favorable physicochemical, pharmacokinetic, and toxicological properties. Together with biochemical, cellular, and in vivo assessments, the results reveal 7.44 as a lead for further optimization towards targeted therapy of t(8;21) AML.

Development of a First-in-Class Small-Molecule Inhibitor of the C-Terminal Hsp90 Dimerization.

Heat shock proteins 90 (Hsp90) are promising therapeutic targets due to their involvement in stabilizing several aberrantly expressed oncoproteins. In cancerous cells, Hsp90 expression is elevated, thereby exerting antiapoptotic effects, which is essential for the malignant transformation and tumor progression. Most of the Hsp90 inhibitors (Hsp90i) under investigation target the ATP binding site in the N-terminal domain of Hsp90. However, adverse effects, including induction of the prosurvival resistance mechanism (heat shock response or HSR) and associated dose-limiting toxicity, have so far precluded their clinical approval. In contrast, modulators that interfere with the C-terminal domain (CTD) of Hsp90 do not inflict HSR. Since the CTD dimerization of Hsp90 is essential for its chaperone activity, interfering with the dimerization process by small-molecule protein-protein interaction inhibitors is a promising strategy for anticancer drug research. We have developed a first-in-class small-molecule inhibitor (5b) targeting the Hsp90 CTD dimerization interface, based on a tripyrimidonamide scaffold through structure-based molecular design, chemical synthesis, binding mode model prediction, assessment of the biochemical affinity, and efficacy against therapy-resistant leukemia cells. 5b reduces xenotransplantation of leukemia cells in zebrafish models and induces apoptosis in BCR-ABL1+ (T315I) tyrosine kinase inhibitor-resistant leukemia cells, without inducing HSR.

C-terminal modulators of heat shock protein of 90 kDa (HSP90): State of development and modes of action.

Cells constantly need to adopt to changing environmental conditions, maintaining homeostasis and proteostasis. Heat shock proteins are a diverse class of molecular chaperones that assist proteins in folding to prevent stress-induced misfolding and aggregation. The heat shock protein of 90 kDa (HSP90) is the most abundant heat shock protein. While basal expression of HSP90 is essential for cell survival, in many tumors elevated HSP90 levels are found, which is often associated with bad prognosis. Therefore, HSP90 has emerged as a major target in tumor therapy. The HSP90 machinery is very complex in that it involves large conformational changes during the chaperoning cycle and a variety of co-chaperones. At the same time, this complexity offers a plethora of possibilities to interfere with HSP90 function. The best characterized class of HSP90 modulators are competitive inhibitors targeting the N-terminal ATP-binding pocket. Nineteen compounds of this class entered clinical trials. However, due to severe adverse effects, including induction of the heat shock response, no N-terminal inhibitor has been approved by the FDA so far. As alternatives, compounds commonly referred to as "C-terminal inhibitors" have been developed, either as natural product-based analogues or by rational design, which employ multiple mechanisms to modulate HSP90 function, including modulation of the interaction with co-chaperones, induction of conformational changes that influence the chaperoning cycle, or inhibition of C-terminal dimerization. In this review, we summarize the current development state of characteristic C-terminal inhibitors, with an emphasis on their (proposed) molecular modes of action and binding sites.

The tetrahydroxanthone-dimer phomoxanthone A is a strong inducer of apoptosis in cisplatin-resistant solid cancer cells.

Platinum compounds are the first-line therapy for many types of cancer. However, drug resistance has frequently been reported for and is a major limitation of platinum-based chemotherapy in the clinic. In the current study, we examined the anti-tumor activity of phomoxanthone A (PXA), a tetrahydroxanthone dimer isolated from the endophytic fungus Phomopsis longicolla, in several solid cancer cell lines and their cisplatin-resistant sub-cell lines. PXA showed strong cytotoxic effects with IC50 values in the high nanomolar or low micromolar range in MTT assays. IC50 values of PXA were lower than those of cisplatin. Remarkably, equipotent anti-cancer activity was found in cisplatin-sensitive and respective cisplatin-resistant cells. Anticancer effects of PXA were studied in further detail in ovarian cancer (A2780) and bladder cancer (J82) cell pairs. PXA led to rapid depolarization of the mitochondrial membrane potential and strong activation of caspase 3 and 7, eventually resulting in strong induction of apoptosis. These effects occurred again both in sensitive and resistant cell lines. IC50 values of PXA from MTT and mitochondrial membrane depolarization assays were in good agreement. Configurational free energy computations indicate that both the neutral and singly negatively charged PXA show membrane partitioning and can penetrate the inner mitochondrial membrane. PXA treatment did not damage the plasma membranes of cancer cells, thus excluding unspecific membrane effects. Further, PXA had neither an effect on intracellular ROS nor on reduction of ROS after hydrogen peroxide treatment. In conclusion, our studies present PXA as a natural compound with strong apoptotic anticancer effects against platinum-resistant solid cancers. This may open new treatment options in clinically resistant malignancies.

Empirical Bayes estimation of posterior probabilities of enrichment: a comparative study of five estimators of the local false discovery rate.

BACKGROUND: In investigating differentially expressed genes or other selected features, researchers conduct hypothesis tests to determine which biological categories, such as those of the Gene Ontology (GO), are enriched for the selected features. Multiple comparison procedures (MCPs) are commonly used to prevent excessive false positive rates. Traditional MCPs, e.g., the Bonferroni method, go to the opposite extreme: strictly controlling a family-wise error rate, resulting in excessive false negative rates. Researchers generally prefer the more balanced approach of instead controlling the false discovery rate (FDR). However, the q-values that methods of FDR control assign to biological categories tend to be too low to reliably estimate the probability that a biological category is not enriched for the preselected features. Thus, we study an application of the other estimators of that probability, which is called the local FDR (LFDR). RESULTS: We considered five LFDR estimators for detecting enriched GO terms: a binomial-based estimator (BBE), a maximum likelihood estimator (MLE), a normalized MLE (NMLE), a histogram-based estimator assuming a theoretical null hypothesis (HBE), and a histogram-based estimator assuming an empirical null hypothesis (HBE-EN). Since NMLE depends not only on the data but also on the specified value of π0, the proportion of non-enriched GO terms, it is only advantageous when either π0 is already known with sufficient accuracy or there are data for only 1 GO term. By contrast, the other estimators work without specifying π0 but require data for at least 2 GO terms. Our simulation studies yielded the following summaries of the relative performance of each of those four estimators. HBE and HBE-EN produced larger biases for 2, 4, 8, 32, and 100 GO terms than BBE and MLE. BBE has the lowest bias if π0 is 1 and if the number of GO terms is between 2 and 32. The bias of MLE is no worse than that of BBE for 100 GO terms even when the ideal number of components in its underlying mixture model is unknown, but has high bias when the number of GO terms is small compared to the number of estimated parameters. For unknown values of π0, BBE has the lowest bias for a small number of GO terms (2-32 GO terms), and MLE has the lowest bias for a medium number of GO terms (100 GO terms). CONCLUSIONS: For enrichment detection, we recommend estimating the LFDR by MLE given at least a medium number of GO terms, by BBE given a small number of GO terms, and by NMLE given either only 1 GO term or precise knowledge of π0.

Estimators of the local false discovery rate designed for small numbers of tests.

Histogram-based empirical Bayes methods developed for analyzing data for large numbers of genes, SNPs, or other biological features tend to have large biases when applied to data with a smaller number of features such as genes with expression measured conventionally, proteins, and metabolites. To analyze such small-scale and medium-scale data in an empirical Bayes framework, we introduce corrections of maximum likelihood estimators (MLEs) of the local false discovery rate (LFDR). In this context, the MLE estimates the LFDR, which is a posterior probability of null hypothesis truth, by estimating the prior distribution. The corrections lie in excluding each feature when estimating one or more parameters on which the prior depends. In addition, we propose the expected LFDR (ELFDR) in order to propagate the uncertainty involved in estimating the prior. We also introduce an optimally weighted combination of the best of the corrected MLEs with a previous estimator that, being based on a binomial distribution, does not require a parametric model of the data distribution across features. An application of the new estimators and previous estimators to protein abundance data illustrates the extent to which different estimators lead to different conclusions about which proteins are affected by cancer. A simulation study was conducted to approximate the bias of the new estimators relative to previous LFDR estimators. Data were simulated for two different numbers of features (N), two different noncentrality parameter values or detectability levels (dalt), and several proportions of unaffected features (p0). One of these previous estimators is a histogram-based estimator (HBE) designed for a large number of features. The simulations show that some of the corrected MLEs and the ELFDR that corrects the HBE reduce the negative bias relative to the MLE and the HBE, respectively. For every method, we defined the worst-case performance as the maximum of the absolute value of the bias over the two different dalt and over various p0. The best worst-case methods represent the safest methods to be used under given conditions. This analysis indicates that the binomial-based method has the lowest worst-case absolute bias for high p0 and for N = 3, 12. However, the corrected MLE that is based on the minimum description length (MDL) principle is the best worst-case method when the value of p0 is more uncertain since it has one of the lowest worst-case biases over all possible values of p0 and for N = 3, 12. Therefore, the safest estimator considered is the binomial-based method when a high proportion of unaffected features can be assumed and the MDL-based method otherwise. A second simulation study was conducted with additional values of N. We found that HBE requires N to be at least 6-12 features to perform as well as the estimators proposed here, with the precise minimum N depending on p0 and dalt.

Degrees of differential gene expression: detecting biologically significant expression differences and estimating their magnitudes.

MOTIVATION: Many methods of identifying differential expression in genes depend on testing the null hypotheses of exactly equal means or distributions of expression levels for each gene across groups, even though a statistically significant difference in the expression level does not imply the occurrence of any difference of biological or clinical significance. This is because a mathematical definition of 'differential expression' as any non-zero difference does not correspond to the differential expression biologists seek. Furthermore, while some current methods account for multiple comparisons in hypothesis tests, they do not accordingly adjust estimates of the degrees to which genes are differentially expressed. Both problems lead to overstating the relevance of findings. RESULTS: Testing whether genes have relevant differential expression can be accomplished with customized null hypotheses, thereby redefining 'differential expression' in a way that is more biologically meaningful. When such tests control the false discovery rate, they effectively discover genes based on a desired quantile of differential gene expression. Estimation of the degree to which genes are differentially expressed has been corrected for multiple comparisons. AVAILABILITY: R code is freely available from http://www.davidbickel.com and may become available from www.r-project.org or www.bioconductor.org SUPPLEMENTARY INFORMATION: Applications to cancer microarrays, an application in the absence of differential expression, pseudocode, and a guide to customizing the methods may be found at www.davidbickel.com and www.mathpreprints.com